mirror of
https://github.com/hwchase17/langchain
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c323742f4f
- **Description:** Adds MistralAIEmbeddings class for embeddings, using the new official API. - **Dependencies:** mistralai - **Tag maintainer**: @efriis, @hwchase17 - **Twitter handle:** @LMS_David_RS Create `integrations/text_embedding/mistralai.ipynb`: an example notebook for MistralAIEmbeddings class Modify `embeddings/__init__.py`: Import the class Create `embeddings/mistralai.py`: The embedding class Create `integration_tests/embeddings/test_mistralai.py`: The test file. --------- Co-authored-by: Erick Friis <erick@langchain.dev>
104 lines
2.1 KiB
Plaintext
104 lines
2.1 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "b14a24db",
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"metadata": {},
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"source": [
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"# MistralAIEmbeddings\n",
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"\n",
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"This notebook explains how to use MistralAIEmbeddings, which is included in the langchain_mistralai package, to embed texts in langchain."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"id": "0ab948fc",
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"metadata": {},
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"outputs": [],
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"source": [
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"# pip install -U langchain-mistralai"
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]
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},
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{
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"cell_type": "markdown",
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"id": "67c637ca",
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"metadata": {},
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"source": [
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"## import the library"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "5709b030",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_mistralai import MistralAIEmbeddings"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "1756b1ba",
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"metadata": {},
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"outputs": [],
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"source": [
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"embedding = MistralAIEmbeddings(mistral_api_key='your-api-key')"
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]
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},
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{
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"cell_type": "markdown",
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"id": "4a2a098d",
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"metadata": {},
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"source": [
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"# Using the Embedding Model\n",
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"With `MistralAIEmbeddings`, you can directly use the default model 'mistral-embed', or set a different one if available."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"id": "584b9af5",
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"metadata": {},
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"outputs": [],
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"source": [
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"embedding.model = 'mistral-embed' # or your preferred model if available"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"id": "be18b873",
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"metadata": {},
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"outputs": [],
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"source": [
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"res_query = embedding.embed_query(\"The test information\")\n",
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"res_document = embedding.embed_documents([\"test1\", \"another test\"])"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.4"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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